Piecewise linear trends and cycles in primary commodity prices

We extend the methodology put forward in Yamada and Yoon (2014, Journal of International Money and Finance, 42(C), 193–207) to analyze the trend and cyclical behavior of relative primary commodity prices. These authors propose the use of the so-called ℓ₁-filter that renders piecewise linear trends of the underlying data. Our focus on the calibration of such filter and its implications for the empirical analysis of primary commodity prices, especially the interpretation given to the resulting trend. We also illustrate how suitably calibrated filters may be used to compute piecewise linear (super) cycles, whose turning points are easy to identify.     

A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the ‘business cycle’

This paper introduces a general procedure for decomposition of non-stationary time series into a permanent and transitory component allowing both components to be stochastic. The permanent component is shown to be a random walk with drift and the transitory or cyclical component is a stationary process with mean zero. The decomposition methodology, which depends only on past data and therefore is computable in ‘real time’, is applied to the problem of measuring and dating business ‘cycles’ in the portwar U.S. economy. We find that measured expansions and contractions are of roughly equivalent duration and that our dating of cyclical episodes tends to lead the traditional NBER dating and, to a lesser extent, the ‘growth cycle’ chronology of Zarnowitz and Boschan (1977).     

Total Factor Productivity Growth in U.S. Commercial Banking for 1935–1991: A Latent Variable Approach Using the Kalman Filter

This paper studies total factor productivity (TFP) in U.S. commercial banking for 1935–1991. TFP can contain a procyclical bias when some input factors are not freely variable, causing their shadow and market prices to differ. We correct this bias in TFP by decomposing it into its latent stochastic trend and cyclical components by employing the Kalman filter. Using the FDIC's annual aggregate data on U.S. insured commercial banks, we report that the stochastic trend has been positive during 1935–1991, with an average annual growth rate of 2.27%, and that it exhibits a decaying time path. We attribute the positive TFP growth to technical change.     

Testing of nonstationary cycles in financial time series data

In this article we propose a method for testing nonstationary cycles in financial time series data. We use a procedure that permits us to test unit root cycles in raw time series. The test has several distinguishing features compared with other procedures. In particular, it has a standard null limit distribution and is the most efficient test when directed against the appropriate (fractional) alternatives. In addition, it allows us to test unit root cycles at each of the frequencies, and, thus, it permits us to approximate the number of periods per cycle. The results, based on the daily structure of Spanish Stock Market prices (IBEX35), show that some intra-year cycles occur, and they take place at approximately 6, 9 or between 24 and 50 periods. The analysis was extended to several other stock market indices of various countries and though the results differ in terms of frequencies, the same conclusions hold, finding evidence of intra-year cyclical effects in all countries. JEL Classification C22; G14.     

Trends and random walks in macroeconmic time series: Some evidence and implications

This paper investigates whether macroeconomic time series are better characterized as stationary fluctuations around a deterministic trend or as non-stationary processes that have no tendency to return to a deterministic path. Using long historical time series for the U.S. we are unable to reject the hypothesis that these series are non-stationary stochastic processes with no tendency to return to a trend line. Based on these findings and an unobserved components model for output that decomposes fluctuations into a secular or growth component and a cyclical component we infer that shocks to the former, which we associate with real disturbances, contribute substantially to the variation in observed output. We conclude that macroeconomic models that focus on monetary disturbances as a source of purely transitory fluctuations may never be successful in explaining a large fraction of output variation and that stochastic variation due to real factors is an essential element of any model of macroeconomic fluctuations.     

Is it one break or ongoing permanent shocks that explains U.S. real GDP?

The relative importance of permanent versus cyclical shocks to GDP has been found to depend on the presence or absence of a single break in mean growth. We estimate unobserved components models conditional on a trend break having occurred in any specified quarter and use the Bayesian model averaging to combine the conditional estimates. We estimate a break occurred around 2006:1. Allowing for a break significantly reduces estimates of trend variance. However, enough spread remains in the posterior distribution to indicate that available data does not definitively settle the question of the relative importance of trend versus cycle.     

Trends,Cycles and Convergence in U.S. Regional House Prices

In this study we present a statistical analysis of the time series properties of the geographic regions in the OFHEO U.S. house price database. The time period for our study is first quarter 1975 through second quarter 2005. We perform an unobserved components, structural time series analysis of nine regional indexes and two super-regional factors and fit a classic “smooth trend plus cycle” model. We then apply bivariate unit root tests for absolute and relative convergence of the regions and factors, allowing for the possibility of a structural break. We find the two super-regions have slightly different patterns of trends and cycles until the early to mid-1990s, when a common pattern of strong and sustained price appreciation is seen. The evidence for regional convergence is mixed, with little for the first super-regional factor and some examples of relative convergence within the second factor. Thus support for a simple error correction model for regional house prices in our study is mixed.     

A cyclical model of exchange rate volatility

In this paper, we investigate the long run dynamics of the intraday range of the GBP/USD, JPY/USD and CHF/USD exchange rates. We use a non-parametric filter to extract the low frequency component of the intraday range, and model the cyclical deviation of the range from the long run trend as a stationary autoregressive process. We use the cyclical volatility model to generate out-of-sample forecasts of exchange rate volatility for horizons of up to 1 year under the assumption that the long run trend is fully persistent. As a benchmark, we compare the forecasts of the cyclical volatility model with those of the range-based EGARCH and FIEGARCH models of Brandt and Jones (2006). Not only does the cyclical volatility model provide a very substantial computational advantage over the EGARCH and FIEGARCH models, but it also offers an improvement in out-of-sample forecast performance.     

Business cycles with staggered prices and international trade in intermediate inputs

International trade in intermediate inputs and, increasingly, in goods produced at multiple stages of processing has been widely studied in the real trade literature. We assess the role of this feature of modern world trade in accounting for some stylized facts about international business cycles. Our model with staggered prices and trade in intermediates across four stages of processing does well in explaining the observed international correlations in aggregate quantities, and it performs much better than a single-stage model with no trade in intermediates. The model in itself does not provide a full account of the cyclical behavior of the real exchange rate, but, compared to the single-stage model, it moves in the right direction.     

Business cycles,financial cycles and capital structure

We perform peridogram based cycle analysis of firm capital structure and find evidence that firms’ leverage is both persistent and cyclical. The cyclicality of leverage is supported by the trade-off, pecking order and market timing capital structure theories (Korajczyk and Levy in J Financ Econ 68:75–109, 2003; Bhamra et al. in Rev Financ Stud 23:645–703, 2010). Although market timing theory research supports persistence, previous literature dictates that the trade-off and pecking order theories may predict either persistent or mean reverting leverage. Our tests reject mean reversion in favor of persistent and cyclical leverage. We corroborate pecking order theory literature that predicts leverage is persistent. In these models, when firms’ investment spending is below earnings, leverage decreases. In addition, we examine whether firms change their capital structure as a result of business and financial cycles. Since financial cycles last longer than business cycles, financial cycles should have a long term effect on leverage. Our findings confirm the persistent leverage business cycle models that suggest firms change their capital structure due to financial and credit cycles (Jermann and Quadrini in Am Econ Rev 102:238–271, 2012; Azariadis et al. in Rev Econ Stud 83:1364–1405, 2016). We conclude that leverage is persistent due to the cyclicality of the financing decision.     

An Unobserved Components Model that Yields Business and Medium‐Run Cycles

We generalize the unobserved components (UC) model to allow the permanent component to have different dynamics than the transitory components when decomposing U.S. economic activity using a multivariate UC model of (log) output, consumption, and investment. We find that these proposed dynamics in the permanent component are statistically significant and distinct from those of the transitory components. Our approach provides an alternative explanation for the growth cycles identified by Comin and Gertler ( 2006 ) that is related to the cyclical movements in technology, in a framework consistent with the Beveridge and Nelson ( 1981 ) decomposition.     

Cyclical Changes in Firm Volatility

We characterize trends and cycles in the volatility of U.S. firms using a measure that we argue more cleanly captures firm‐specific volatility in sales and earnings growth than standard measures do. While earlier literature has emphasized a trend increase in the volatility of publicly traded firms, we find that a typical publicly traded firm has become more stable. We find that the negative association between firm‐specific volatility and the business cycle is weaker than earlier research based on dispersion measures suggests. We find that during the Great Recession of 2007–2009, firm‐specific volatility increased moderately but never substantially exceeded its sample mean. Our results are inconsistent with the hypothesis that firm‐specific volatility is an important driver of the business cycle, as it theoretically could be through an effect of default risk on credit spreads.     

Action research cycles for multiple futures perspectives

This paper focuses on the cyclical and iterative processes of action research and their usefulness in enabling participants in futures work to expand their images of futures. The author has been developing a participatory method of scenario development, based on action research, using cycles within cycles, thus allowing multiple opportunities for reflection and reperception. Because people can find it difficult to perceive their potential futures, to examine possibilities from different angles can clarify problems and help participants develop their reactions to various futures. This paper presents a case study of the new method, working through a series of cycles with a credit union, arguing that a cycles-within-cycles-within-cycles process has the potential to help make explicit the concealed and subconscious forces affecting the future of the participants' social entity.     

Let's take a break: Trends and cycles in US real GDP

Trend-cycle decompositions for US real GDP such as the unobserved components models, the Beveridge-Nelson decomposition, the Hodrick-Prescott filter and others yield very different cycles which bear little resemblance to the NBER chronology, ascribes much movements to the trend leaving little to the cycle, and some imply a negative correlation between the noise to the cycle and the trend. We argue that these features are artifacts created by the neglect of a change in the slope of the trend function. Once this is accounted for, all methods yield the same cycle with a trend that is non-stochastic except for a few periods around 1973. The cycle is more important in magnitude than previously reported and it accords well with the NBER chronology. Our results are corroborated using an alternative trend-cycle decomposition based on a generalized unobserved components models with errors having a mixture of normals distribution for both the slope of the trend function and the cyclical component.     

Immigration,remittances and business cycles

Using data on border enforcement and macroeconomic indicators from the U.S. and Mexico, we estimate a two-country business cycle model of labor migration and remittances. The model matches the cyclical dynamics of unskilled migration, and documents the insurance role of remittances in consumption smoothing. Over the cycle, immigration increases with the expected stream of future wage gains, but it is dampened by a sunk emigration cost. Migration barriers slow the adjustment of the stock of immigrant labor, enhancing the volatility of unskilled wages and remittances. Changes in border enforcement have asymmetric welfare implications for the skilled and unskilled households.     

Demand and productivity components of business cycles: Estimates and implications

Standard stochastic growth models provide theoretical restrictions on output decomposition which can be used to investigate whether productivity shocks played a major role in observed business cycles. Applying these restrictions to US data leads to the following findings: (i) Business cycles implied by productivity shocks are mildly correlated to overall fluctuations and help account for a few episodes of US postwar recessions. However, only 20% of US fluctuations can be explained by these shocks. (ii) Most fluctuations seem instead to be due to “nominal demand” shocks, i.e. shocks which move output and prices in the same direction, but whose effects on output are ultimately transitory. (iii) Canonical sticky price models in the new-neoclassical synthesis tradition can account for the cyclical comovements of output and prices, but canonical, frictionless, RBC models cannot.     

Technology,Utilization, and Inflation: What Drives the New Keynesian Phillips Curve?

We argue that the New Keynesian Phillips Curve literature has failed to deliver a convincing measure of real marginal costs. We start from a careful modeling of optimal price setting allowing for nonunitary factor substitution, nonneutral technical change, and time‐varying factor utilization rates. This ensures the resulting real marginal cost measures match volatility reductions and level changes witnessed in many U.S. time series. The cost measure comprises conventional countercyclical cost elements plus procyclical (and covarying) utilization rates. Although procyclical elements seem to dominate, the components of real marginal cost components are becoming less cyclical over time. Incorporating this richer driving variable produces more plausible price‐stickiness estimates than otherwise and suggests a more balanced weight of backward‐ and forward‐looking inflation expectations than commonly found. Our results challenge existing views of inflation determinants and have important implications for modeling inflation in New Keynesian models.     

Credit cycles and macro fundamentals

We use an intensity-based framework to study the relation between macroeconomic fundamentals and cycles in defaults and rating activity. Using Standard and Poor's U.S. corporate rating transition and default data over the period 1980–2005, we directly estimate the default and rating cycle from micro data. We relate this cycle to the business cycle, bank lending conditions, and financial market variables. In line with earlier studies, the macro variables appear to explain part of the default cycle. However, we strongly reject the correct dynamic specification of these models. The problem is solved by adding an unobserved dynamic component to the model, which can be interpreted as an omitted systematic credit risk factor. By accounting for this latent factor, many of the observed macro variables loose their significance. There are a few exceptions, but the economic impact of the observed macro variables for credit risk remains low. We also show that systematic credit risk factors differ over transition types, with risk factors for downgrades being noticeably different from those for upgrades. We conclude that portfolio credit risk models based only on observable systematic risk factors omit one of the strongest determinants of credit risk at the portfolio level. This has obvious consequences for current modeling and risk management practices.     

Nonlinear duration dependence in stock market cycles

We reexamine duration dependence in stock market cycles using a generalized Weibull model. Recent empirical work by Cochran and DeFina [Cochran, S. J., & DeFina, R. H. (1995). Duration dependence in the U.S. stock market cycle: A parametric approach. Applied Financial Economics, 5, 309-318.], who use a chronology of stock market cycles to estimate a Weibull hazard model, shows duration dependence in stock prices. They find evidence of duration dependence in prewar market expansions and postwar market contractions. We update their postwar sample, then use a more flexible model that finds evidence of duration dependence for all prewar and postwar samples. The generalized Weibull model is shown to be statistically superior to the conventional Weibull model for all samples except prewar expansions.     

International house price cycles,monetary policy and credit

We evaluate three alternative predictors of house price corrections: anticipated tightenings of monetary policy, deviations of house prices from fundamentals, and rapid credit growth. A new cross-country measure of monetary policy expectations based on an international term structure model with time-varying risk premiums is constructed. House price overvaluation is estimated via an asset pricing model. The variables are incorporated into a panel logit regression model that estimates the likelihood of a large house price correction in 18 OECD countries. The results show that corrections are predicted by increases in the market’s forecast of higher policy rates. The estimated degree of house price overvaluation also contains significant information about subsequent price reversals. In contrast to the financial crisis literature, credit growth is less important. All of these variables help forecast recessions.